Single linkage method

Single linkage methods have a tendency to chain together ill-defined clusters (see Fig. 30.8). This eventually leads to include rather different subjects (A to X of Fig. 30.8) into the same long drawn-out cluster. For that reason one sometimes... 

There are a variety of ways to compute distances between data points and clusters in hierarchical clustering. A few are shown in Figure 9.7. The single-linkage method... 

Different algorithms may produce radically different results when applied to the same dataset. For example, the single-linkage method has difficulty in recognizing two separate clusters linked by a chain of intervening observations (a situation par-... 

Apply the single linkage method to the (unstandardized) data in Table 8.1. 

The result of a cluster analysis using a single linkage method (without transformation of data) is given in the two dendrograms of FIGURES 4 and 5. 

There is a wide variety of hierarchical algorithms available and it is impossible to discuss all of them here. Therefore, we shall only explain the most typical ones, namely the single linkage, the complete linkage and the average linkage methods. 

There exist several methods of hierarchical clustering which use diverse measures of distance or similarity, respectively, e.g., single linkage, complete linkage, average linkage, centroid linkage, and Ward s method (Sharaf et al. [1986], Massart et al. [1988], Otto [1998] Danzer et al. [2001]). 

Compute the distances between all clusters using complete linkage, single linkage, average linkage, or other methods. 

Of the four different methods of cluster analysis applied, the method of Ward described in the Clustan User Manual (10), worked best when compared to the single-, complete-, or average-linkage methods. Using Ward s method, two clusters, Gn and Gm, are fused when by pooling the variance within two existing clusters the variance of the so formed clusters increases minimally. The variance or the sum of squares within the classes will be chosen as the index h of a partition. 

---